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How Can Artificial Intelligence Help Us
Recently, the media has spent an increasing amount of broadcast time on new technology. The focus of high-tech media has been aimed at the flurry of advances concerning artificial intelligence (AI). What is artificial intelligence and what is the media talking about? Are these technologies beneficial to our society or mere novelties among business and marketing professionals? AI has changed medical facilities, police departments, and manufacturing plants, but how? These questions and many others are the concern of the general public brought about by the lack of education concerning rapidly advancing computer technology.
Artificial intelligence is defined as the ability of a machine to think for itself. Scientists and theorists continue to debate if computers will actually be able to think for themselves at one point (Patterson 7). The generally accepted theory is that computers do and will think more in the future. AI has grown rapidly in the last ten years chiefly because of the advances in computer architecture. The term artificial intelligence was actually coined in 1956 by a group of scientists having their first meeting on the topic (Patterson 6). Early attempts at AI were neural networks modeled after the ones in the human brain. Success was minimal at best because of the lack of computer technology needed to calculate such large equations.
AI is achieved using a number of different methods. The more popular implementations comprise neural networks, chaos engineering, fuzzy logic, knowledge based systems, and expert systems. Using any one of the aforementioned design structures requires a specialized computer system. For example, Anderson Consulting applies a knowledge-based system to commercial loan officers using multimedia (Hedburg 121). Their system requires a fast IBM desktop computer. Other systems may require even more horsepower using exotic computers or workstations. Even more exotic is the software that is used. Since there are very few applications that are pre-written using AI, each company has to write it's own software for the solution to the problem. An easier way around this obstacle is to design an add-on. The company FuziWare makes several applications that act as an addition to a larger application. FuziCalc, FuziQuote, FuziCell, FuziChoice, and FuziCost are all products that are use
d as management decision support systems for other off-the shelf applications (Barron 111).
In order to tell that AI is present we must be able to measure the intelligence being used. For a relative scale of reference, large supercomputers can only create a brain the size of a fly (Butler and Caudill 5). It is surprising what a computer can do with that intelligence once it has been put to work. Almost any scientific, business, or financial profession can benefit greatly from AI. The ability of the computer to analyze variables provides a great advantage to these fields.
There are many ways that AI can be used to solve a problem. Virtually all of these methods require special hardware and software to use them. Unfortunately, that makes AI systems expensive. Consulting firms, companies that design computing solutions for their clients, have offset that cost with the quality of the system. Many new AI systems now give a special edge that is needed to beat the competition.
Neural networks have entered the spotlight with surprisingly successful results. A neural network is a type of information processing system whose architecture is similar to the structure of biological neural systems (Butler and Caudill 5). The neural network tries to mimic the way a brain and nervous system work by analyzing sensory inputs and calculating an outcome. A neural network is usually composed of simple decision making elements that are connected with variable weights and strengths. Each one these elements is called a neurode. The term neurode is similar to the biological neuron. The term was modified slightly to indicate an artificial nature. Memory is stored by a certain pattern of the connection weights between the neurodes. Processing information is performed by changing and spreading the connection's weights among the network. Before it can be used a neural network must be trained. Some can learn by themselves, some require training by doing, and oth!
ers learn by trial and error. A computer learns by naturally associating items the computer is taught and grouping them together physically. Additionally, a computer can retrieve stored information from incomplete or partially incorrect clues. Neural networks are able to generalize categories based on specifics of the contents. Lastly, it is highly fault tolerant. This means that the network can sustain a large amount of damage and still function. Its performance fades proportionally as the neurodes disappear (Butler and Caudill 8). This type of system is inherently an excellent design for any application that requires little human intervention and that must learn on the go.
Created by Lotfi Zadeh almost thirty years ago, fuzzy logic is a mathematical system that deals with imprecise descriptions, such as "new", "nice", or "large" (Schmuller 14). This concept was also inspired from biological roots. The inherent vagueness in everyday life motivates fuzzy logic systems (Schmuller 8). In contrast to the usual yes and no answers, this type of system can distinguish the shades in-between. In Los Angeles a fuzzy logic system is used to analyze input from several cameras located at different intersections (Barron 114). This system provides a "smart light" that can decide whether a traffic light should be changed more often or remain green longer. In order for these "smart lights" to work the system assigns a value to an input and analyzes all the inputs at once. Those inputs that have the highest value get the highest amount of attention. For example, here is how a fuzzy logic system might evaluate water temperature. If the water is cold, it ass!
igns a value of zero. If it is hot the system will assign the value of one. But if the next sample is lukewarm it has the capability to decide upon a value of 0.6 (Schmuller 14). The varying degrees of warmness or coldness are shown through the values assigned to it. Fuzzy logic's structure allows it to easily rate any input and decide upon the importance. Moreover, fuzzy logic lends itself to multiple operations at once.
Fuzzy logic's ability to do multiple operations allows it to be integrated into neural networks. Two very powerful intelligent structures make for an extremely useful product. This integration takes the pros of fuzzy logic and neural networks and eliminates the cons of both systems (Liebowitz 113). This new system is a now a neural network with the ability to learn using fuzzy logic instead of hard concrete facts. Allowing a more fuzzy input to be used in the neural network instead of being passed up will greatly decrease the learning time of such a network.
Another promising arena of AI is chaos engineering. The chaos theory is the cutting-edge mathematical discipline aimed at making sense of the ineffable and finding order among seemingly random events (Weiss 138). Chaologists are experimenting with Wall Street where they are hardly receiving a warm welcome. Nevertheless, chaos engineering has already proven itself and will be present for the foreseeable future. The theory came to life in 1963 at the Massachusetts Institute of Technology. Edward Lorenz, who was frustrated with weather predictions noted that they were inaccurate because of the tiny variations in the data. Over time he noticed that these variations were magnified as time continued. His work went unnoticed until 1975 when James Yorke detailed the findings to American Mathematical Monthly. Yorke's work was the foundation of the modern chaos theory (Weiss 139). The theory is put into practice by using mathematics to model complex natural phenomena.
The chaos theory is used to construct portfolio's of long and short positions in the stock market on Wall Street. This is used to assess market risk accurately, not to predict the future (Weiss 139). Unfortunately, the hard part is putting the theory into practice. It has yet to impress the people that really count: financial officers, corporate treasurers, etc. It is quite understandable though, who is willing to sink money into a system that they cannot understand? Until a track record is set for chaos most will be unwilling to try, but to get the track record someone has to try it, it's what is known as the "catch-22." The chaos theory can be useful in other places as well. Kazuyuki Aihara, an engineering professor at Tokyo's Denki University, claims that chaos engineering can be applied to analyzing heart patients. The pattern of beating hearth changes slightly and each person pattern is different (Ono 41). Considering this discovery a dataprocessing company in J!
apan has marketed a physical checkup system that uses chaos engineering. This system measures health and psychological condition by monitoring changes in circulation at the fingertip (Ono 41). Aihara admits that chaos-engineering has tremendous potential but does have limitations. He states, "It can predict the future more accurately than any other system but that doesn't mean it can predict the future all the time." Along these lines Rabi Satter, a computer consultant with a BS in Computer Science, believes that the current sentiment that the world is rational and can be reduced to mathematical equations is wrong. "In order to make great strides in this arena [AI] we need new approaches informed by the past but not guided by it. A fresh voice if you would. As one person said we are using brute force to solve the problem" states Satter.
A few more implementations of artificial intelligence include knowledge-based systems, expert systems, and case-based reasoning. All of th
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